VISUALIZING FOREST FIRE RISKS: AN EFFICIENT AND RESILIENT IOT-ENABLED WIRELESS SENSOR NETWORK FRAMEWORK

Zade Mahesh  Mahadev; Rahul Kumar  Budania; Shrinivas Tanaji  Shirkande; Bansude Vijaysinh  Uttamrao

doi:10.29121/shodhkosh.v7.i5s.2026.7542

Authors

Zade Mahesh Mahadev Research Scholar, Shri Jagdish Prasad Jhabarmal Tibrewala University, Rajasthan, India, and Assistant Professor, S. K. N. Sinhgad College of Engineering, Korti, Pandharpur, Maharashtra, India
Dr. Rahul Kumar Budania Associate Professor, Shri Jagdish Prasad Jhabarmal Tibrewala University, Rajasthan, India
Dr. Shrinivas Tanaji Shirkande Associate Professor at S. B. Patil College of Engineering, Indapur, Maharashtra, India
Bansude Vijaysinh Uttamrao Research Scholar at Shri Jagdish Prasad Jhabarmal Tibrewala University, Rajasthan and Assistant Professor at S. B. Patil College of Engineering, Indapur, Maharashtra, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i5s.2026.7542

Keywords:

Forest Fire Detection, Iot, Wireless Sensor Networks Wsn, Fire Risk Visualization, Edge–Cloud Computing, Fire Risk Index Fri, Environmental Monitoring, Smart Forest Systems, Real-Time Analytics, Disaster Management

Abstract [English]

Forest fires may be of great danger to the environmental sustainability, biodiversity and human life and thus highly sophisticated monitoring and early warning systems are sought. Conventional fire detection systems are usually characterized by slow response, poor coverage, and unreal-time information. In order to overcome these issues, this paper will present a secure and robust Internet of Things (IoT)-enabled Wireless Sensor Network (WSN) architecture of real-time visualization of forest fire risks. The suggested system incorporates distributed sensor nodes that are then used to continuously measure the environmental parameters in terms of temperature, humidity, smoke concentration, and the speed of wind. A multi-parameter Fire Risk Index (FRI) model is created to estimate the fire risk dynamically, whereas edge -cloud computing is used to process the data in the best way, minimize the latency and increase the system scalability. The framework also embraces the use of the latest visualization tools such as heatmaps and Geographic Information System (GIS)-based dashboards to convert the raw sensor data into actionable information to be used by the decision-makers. The experimental findings prove that the suggested strategy has better performance in terms of prediction accuracy, reduced energy usage, latency minimization, throughput, and network lifetime than the traditional and existing IoT-based systems. Real-time monitoring along with an intuitive visualization can also enhance the situational awareness to perform the active fire control. In general, the proposed framework will offer a powerful and scalable intelligent forest fire risk assessment and disaster mitigation solution.

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